Cohort study of the relationship between individual psychotherapy and pregnancy outcomes.

INTRODUCTION: Antenatal depression is associated with poor obstetric outcomes, but it has not been determined if treatment improves these outcomes. We hypothesized that psychotherapy for antenatal depression would decrease rates of low Apgar score, preterm birth, low birthweight, and high maternal weight gain. METHODS: Using longitudinal clinical data from the electronic health record (EHR) of a large academic medical center, we examined the association between exposure to psychotherapy during pregnancy among women with a history of major depressive disorder and obstetric outcomes. We compared outcomes between women with and without psychotherapy treatment during pregnancy, and included a dose response analysis. RESULTS: Of 50,856 women with pregnancies between 1998 and 2013, 5413 had a lifetime diagnosis of depression (948 had a diagnosis of depression during pregnancy), and 536 received psychotherapy at least once during pregnancy. Women who received one or more psychotherapy sessions during pregnancy had increased odds of preterm delivery and decreased odds of high maternal weight gain (more than 40 pounds). Individuals who received four or more psychotherapy sessions during pregnancy had increased odds of preterm birth and low infant birth weight and decreased odds of high maternal weight gain. LIMITATIONS: Patients may have pursued treatment outside of this hospital's EHR data, and we cannot control for the quality of treatment or type of psychotherapy. DISCUSSION: Psychotherapy was associated with negative obstetric outcomes. While treatment of depression in pregnant women has been shown to benefit the mother, the absence of benefit in terms of pregnancy outcomes merits further investigation.

Impairment in delay discounting in schizophrenia and schizoaffective disorder but not primary mood disorders.

A measure of planning and impulse control, the delay-discounting (DD) task estimates the extent to which an individual decreases the perceived value of a reward as the reward is delayed. We examined cross-disorder performance between healthy controls (n = 88), individuals with bipolar disorder (n = 23), major depressive disorder (n = 43), and primary psychotic disorders (schizophrenia and schizoaffective disorder; n = 51) on the DD task (using a $10 delayed larger reward), as well as the interaction of DD scores with other symptom domains (cognition, psychosis, and affect). We found that individuals with schizophrenia and schizoaffective disorder display significantly greater rates of discounting compared to healthy controls, while individuals with a primary mood disorder do not differ from healthy controls after adjustment for IQ. Further, impairment in working memory is associated with higher discounting rates among individuals with schizophrenia and schizoaffective disorder, but cognitive dysfunction alone does not account for the extent of impairment in DD. Taken together, these results suggest an impaired ability to plan for the future and make adaptive decisions that are specific to individuals with psychotic disorders, and likely related to adverse functional outcomes. More generally, this work demonstrates the presence of variation in impulsivity across major psychiatric illnesses, supporting the use of a trans-diagnostic perspective.

Efficient genome-wide association in biobanks using topic modeling identifies multiple novel disease loci.

Biobanks and national registries represent a powerful tool for genomic discovery, but rely on diagnostic codes that may be unreliable and fail to capture the relationship between related diagnoses. We developed an efficient means of conducting genome-wide association studies using combinations of diagnostic codes from electronic health records (EHR) for 10845 participants in a biobanking program at two large academic medical centers. Specifically, we applied latent Dirichilet allocation to fit 50 disease topics based on diagnostic codes, then conducted genome-wide common-variant association for each topic. In sensitivity analysis, these results were contrasted with those obtained from traditional single-diagnosis phenome-wide association analysis, as well as those in which only a subset of diagnostic codes are included per topic. In meta-analysis across three biobank cohorts, we identified 23 disease-associated loci with p<1e-15, including previously associated autoimmune disease loci. In all cases, observed significant associations were of greater magnitude than for single phenome-wide diagnostic codes, and incorporation of less strongly-loading diagnostic codes enhanced association. This strategy provides a more efficient means of phenome-wide association in biobanks with coded clinical data.

Enhancing Delirium Case Definitions in Electronic Health Records Using Clinical Free Text.

BACKGROUND: Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically ill populations. Delirium is preventable and treatable when diagnosed but the diagnosis is often missed. This important and difficult diagnosis is an attractive candidate for computer-aided decision support if it can be reliably identified at scale. OBJECTIVE: Here, using an electronic health record-based case definition of delirium, we characterize incidence of this highly morbid condition in 2 large academic medical centers. METHODS: Using the electronic health record of 2 large New England academic medical centers, we calculated and compared the rate of the diagnosis of delirium using a range of administrative and discharge summary text-based case definitions over an 8-year period. RESULTS: Depending on case definitions, the overall delirium rate ranged from 2.0-5.4% of 809,512 admissions identified. The identified rate of delirium increased between 2005 and 2013, such that by the final year of the study, one of the two sites reported delirium in 7.0% of cases. The concordance between case definitions was low; only half of the cases identified by text analysis were captured by administrative data. CONCLUSION: Delirium may be better captured by composite outcomes, including both administrative claims data and elements drawn from unstructured data sources. That the rate of delirium observed in this study is far lower than the current literature estimates suggests that further work on case definitions, identification, and documented diagnosis is required.

Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing.

IMPORTANCE: Suicide represents the 10th leading cause of death across age groups in the United States (12.6 cases per 100 000) and remains challenging to predict. While many individuals who die by suicide are seen by physicians before their attempt, they may not seek psychiatric care. OBJECTIVE: To determine the extent to which incorporating natural language processing of narrative discharge notes improves stratification of risk for death by suicide after medical or surgical hospital discharge. DESIGN, SETTING, AND PARTICIPANTS: In this retrospective health care use study, clinical data were analyzed from individuals with discharges from 2 large academic medical centers between January 1, 2005, and December 31, 2013. MAIN OUTCOMES AND MEASURES: The primary outcome was suicide as a reported cause of death based on Massachusetts Department of Public Health records. Regression models for prediction of death by suicide or accidental death were compared relying solely on coded clinical data and those using natural language processing of hospital discharge notes. RESULTS: There were 845 417 hospital discharges represented in the cohort, including 458 053 unique individuals. Overall, all-cause mortality was 18% during 9 years, and the median follow-up was 5.2 years. The cohort included 235 (0.1%) who died by suicide during 2.4 million patient-years of follow-up. Positive valence reflected in narrative notes was associated with a 30% reduction in risk for suicide in models adjusted for coded sociodemographic and clinical features (hazard ratio, 0.70; 95% CI, 0.58-0.85; P < .001) and improved model fit (χ22 = 14.843, P < .001 by log-likelihood test). The C statistic was 0.741 (95% CI, 0.738-0.744) for models of suicide with or without inclusion of accidental death. CONCLUSIONS AND RELEVANCE: Multiple clinical features available at hospital discharge identified a cohort of individuals at substantially increased risk for suicide. Greater positive valence expressed in narrative discharge summaries was associated with substantially diminished risk. Automated tools to aid clinicians in evaluating these risks may assist in identifying high-risk individuals.